Financial institutions desire to assist current customers by reaching out to them in an effective manner to help them manage their financial accounts. For example, financial institutions may reach out to customers when the customer is eligible for a special type of account, when the customer may benefit from refinancing, or for other reasons. In another example, financial institutions may assist customers in remembering due dates for payments by providing a reminder to the customer of the upcoming bill. These contacts, however, cost the financial institution in the form of the expense of mailing a letter, or the resources required to send an email or call a customer. Still further, some individuals may not be influenced by the contact. For example, some customers may decide to register for the account without being contacted. Thus, some contacts are wasteful use of resources because the contact is not necessary or does not have the desired effect.
Financial institutions also desire to use their resources in the most efficient manner. The financial institution may have limited resources for contacting customers and thus desire to contact those customers most receptive to the contact. Unfortunately, the financial institutions are not efficient in determining which individuals should be contacted and therefore the institutions waste money on contacting individuals unnecessarily.
A traditional approach to determine which individuals should be contacted includes building two separate regression models, one for the test population (or treated population) and one for the control population (or untreated population), and then taking the difference between the two models. This “two model then differencing” approach, however, does not work well in ordering the incremental effect. That is, this approach is insufficient for determining the incremental effect given underlying variation in the population. A major drawback of this traditional approach is that it models the test data and the control data separately. When the test data and the control data are treated separately, the decision variable cannot be included in either the test model or the control model. A variable might be very predictive in either the test model, the control model, or even in both, but there is no guarantee that this variable is decision relevant, because there is no way to test the significance of the interaction between this variable and the decision variable when the test data and the control data are studied separately.
Additional issues that make modeling the incremental effect (or net effect) of a treatment on individuals difficult include that individuals cannot be in both a treatment group and a control group, that the impact of the variable on incremental effect is typically non-linear, that there may be a very low signal to noise ratio (e.g., there may be a weak signal and a strong noise), and that the variables may not be stable over time. The traditional approaches do not adequately address these issues.
A more precise incremental effect model is beneficial because individuals can be targeted with a greater degree of certainty. In the past, individuals were either treated randomly or treated using models that did not reliably predict the incremental effect giving the underlying variation within the populations. All of these issues can add up to wasted time, effort, and expense for financial institutions as they consider which individuals to assist through providing a treatment. Furthermore, the inefficient application of the treatment increases costs to the financial institution.